These three misplaced concerns are inherently linked on the overarching focus on 'fake news' as full-length, coherent, traditional news articles. I suggest that we should not consider these models in a strict relationship to "fake news" articles but also fake content, more generally, including false or misleading article summaries, fake user-generated comments, fake and misleading reviews, and falsified profiles, and fake or misleading micro-blogs. In particular, we should concern ourselves with generated organic user posts and comments.

In this blogpost, I focus on conversations regarding the recent releases of Grover. This post is not an indictment of the release strategy of the model as I found the release strategy to be well planned. Rather, I comment on the discussions surrounding the model releases, and troubling trends in prioritizing model releases in strict relation to full-length fake news articles.

Premise 1: It will take a state actor to develop a language model system that will have large scale political or social impact

Low Technical Expertise: The technical expertise necessary to fine-tune these models, scaffold, and operationalize them are also low and require only basic machine learning and coding knowledge. I have shown previously that the barrier to entry for developing systems to disseminate misleading posts and comments is nearly at novice/junior software developer levels. After the release of GPT-2 there was a flurry of activity to develop scripts for fine-tuning and Venmo Screenshots Venmo Venmo Venmo Screenshots Screenshots Screenshotsthemodel, including python packages and one-liner command line interfaces. Actors with basic coding knowledge can easily gather data and fine-tune these models, and users with little-to-no coding skills can download and run operationalized models with simple terminal commands.

Premise 2: We should be more concerned about the use of language models in the development of "fake news" in article form rather than other forms of false content.

Consistently producing passable full-length fake news articles will prove to be much harder than fake user generated comments. Whereas fake full-length news articles should be: 1. in the target domain, 2. up to date with current events, 3. factually plausible, and Drivers License Diary Juicenovag’s California - 4. posted to a site with some institutional credibility, the bar is not as high for user based comments. Ultimately, operationalized models will have to fight model rot that moves at or near the speed of the news cycle. Alternatively, I have shown that generated comments from the 117M model, when posted in the wild, are passable as real user comments. To support this theory, I developed a reddit bot that would read comments from subreddits and respond with generated comments, using the initial reddit comment as a prompt for the model. These generated comments typically pointed towards a general political ideology rather than a particular current event. As such the comments were more plausible. FCo Keren Uunilohi info Desaindotcom Card Id Desain, and a followup on the GPT-2-345M model can be found here.

Defending Against Neural Fake News provides clear evidence that Grover performs best in defending against Grover's fake news. As stated in the paper, Grover's discriminator detects roughly 90% of text generated by Grover across all ranges and sizes. While this is an impressive result in theory, in practice identifying and removing Grover generated false information will prove to be a challenging task.

What should platforms do? Video-sharing platforms like YouTube use deep neural networks to scan videos while they are uploaded, to filter out content like pornography (Hosseini et al., 2017). We suggest platforms do the same for news articles. An ensemble of deep generative models, such as Grover, can analyze the content of text – together with more shallow models that predict humanwritten disinformation. However, humans must still be in the loop due to dangers of flagging real news as machine-generated, and possible unwanted social biases of these models.

This proposed model could stand when assuming that:

A. Most platforms have the capacity to run discriminators like Grover's across their text, presumably with a fast response to remove the items

B. The discriminator will mainly be applied to fake news articles rather than generated comments, summaries, and other content

However, I argue that it would be generally infeasible.

A. Most platforms have the capacity to run discriminators like Grover across their text, presumably with a fast response to remove the items:

While one could assume that larger platforms like Facebook and Twitter might operationalize Grover to detect false information, smaller online communities and platforms will certainly remain at risk. In the same breath, many of the marginalized communities that are vulnerable to false information are not on the major platforms.